Advanced attribution modeling is used to track and score the effect of multiple marketing events leading up to an event… It is also more expensive and time-consuming to manage. Worse, the lack of industry consensus on what makes a good measurement model further hinders marketer adoption. For those reasons, multichannel attribution modeling is not widely used. eMarketer estimates that only 30.1% of US companies that are engaged with more than one digital marketing channel currently use multichannel attribution models.

Insight into your most effective campaigns translates to new investment and drives top-line revenue;

Clear illustration of cross-channel interaction can break down silos and increase cooperation between brand and performance teams who historically operate independently.

Customer journeys are shrouded in the fog of war but multichannel attribution — with executive support and good project management — can help pierce that fog. Successful data-driven attribution requires long-term capital investments but, compared to building inhouse models, requires relatively smaller investments in people. This is a material concern in today’s shallow market for analytics talent; hiring data scientists is increasingly difficult. For these reasons and more, data-driven attribution is a winning recipe for many marketers.

Like most things in life, data-driven attribution is easier said than done. At best, data-driven attribution is complex and expensive but directly increases businesses outcomes. At worst, data-driven attribution is a feedback loop for bad strategy that constrains marketers from taking calculated risks. As you evaluate the potential of data-driven attribution for your organization I encourage you to keep two principles in mind.

Garbage in leads to garbage out. When an attribution model does not show uplift your first reaction should be to examine strategy and tactics, not cut investment. Are we reaching consumers throughout their decision journey? Is our content and creative aligned with context? Mobile illustrates this problem well. The mobile gap to parity — 8% of ad spend on 24% of media consumption — is starting to close as marketers better understand cross-device journeys and put new measurement tools to good use. Attribution models might suggest devaluation of mobile but smart marketers read between the lines.

There is no single source of truth. It’s easy to lose sight that attribution is a means to an end, not an end in itself. To use another cliche: when you have a hammer, everything looks like a nail. Rigorous controlled experiments, deployed consistently, are an easy way to validate model output. No attribution model, data-driven or otherwise, will tell you the value of the strategy you don’t adopt. Experiments can help your team to develop institutional knowledge and take calculated risks on new strategies or ideas.

Is data-driven attribution better than last click? Almost always. Will data-driven attribution solve all your marketing challenges? Of course not! Reasonable expectations — about attribution generally and data-driven attribution specifically — are a smart starting point to cut through the hype. Here’s hoping 2016 is the year of attribution.